Tech companies are shifting more towards machine learning or AI-first strategy. What does that mean to them? What does that mean to us? Growing number of aging population and shrinking funding are putting enormous pressure to the overall healthcare system for keeping up with the desired quality of care with limited resources. Thus, there is an increasing focus on machine learning capabilities for just-in-time alerts to predict various future events so that undesirable incidents can be reduced by giving attention to the right people at the right time. This talk explores the underlying framework behind such capabilities, various strategies for a resilient system, and the role of a machine learning platform.

1. Resilient Machine Learning Systems
for Health Analytics
Mahfuzul Haque
@mahfuzul2012
24/12/2016

2. Challenges
Government
Care Giver Care Receiver
Funding
Cost of service Number of care receiver
Quality of care
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3. http://www.zdnet.com/article/samsung-deakin-uni-to-trial-in-home-a-i-aged-care-technology
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4. Just-in-time Alerts!
17
Number of falls in
last 7 days
23
Likelihood of falls
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5. www.electronicsweekly.com/news/research-news/sensors-predict-elderly-falls-without-wearables-2016-08/
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6. http://www.mobihealthnews.com/39851/wearable-tech-could-more-accurately-predict-falls-assess-ms-disease-state
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7. Fall prediction based on clinical data
Morse Fall Scale (1985)
– History of falling
– Secondary diagnosis
– Use of ambulatory aid
– Intravenous therapy
– Gait
– Mental status
Hendrich II Fall Risk Model (2003)
Johns Hopkins Fall Risk Assessment Tool (2005)
http://www.griswoldhomecare.com/blog/fall-risk-assessment-tools-whats-your-risk/
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8. Machine Learning Pipeline
Get the Data
Explore,
clean, and
refine the
data
Build and
evaluate the
prediction
model
Publish the
model API
Keep
updating
the model
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9. Model Building: Strategy 1
Data
(org 1)
Data
(org 2)
Data
(org 3)
Combine
data
Clean and
refine data
Build and
evaluate
model
Model API for all
organizations
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10. Model Building: Strategy 2
Data
(org 1)
Clean and
refine data
Build and
evaluate
model
Model API for
org 1
Data
(org 2)
Clean and
refine data
Build and
evaluate
model
Model API for
org 2
Data
(org 3)
Clean and
refine data
Build and
evaluate
model
Model API for
org 3
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11. Things to consider
• Same model for all organizations
• Different model for each organization
• Model for new organization
• Different product versions
• Scaling – storage, processing, API end-points
• Where will the pipeline execute?
• Predicting multiple variables
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12. Machine Learning Platforms
http://www.ibm.com/cloud-computing/bluemix
https://aws.amazon.com/machine-learning
https://azure.microsoft.com/ml
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13. Let’s dive into Azure Machine Learning
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14. Let’s dive into Azure Machine Learning
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15. Forming a cross-functional team
• Problem solving
• Domain experts / SMEs
• Machine learning
• Cloud / architecture
• DevOps
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16. Key Lessons
• Bring in expertise by forming the right
cross-functional team.
• Choose a suitable cloud-based machine
learning platform (e.g., Azure ML).
• Keep experimenting.
• Keep improving.
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17. Thank You!
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